Including unlabeled instances in a survival analysis task leads to three distinct subsets of data, that differ in the amount of supervised information they contain: a set of (1) fully observed, (2) partially observed (censored), and (3) unobserved data points. Our goal is to look at these three subsets of data altogether. In particular, we address two research questions: (1) can the predictive performance over an independent test set be increased by including unlabeled instances (i.e., does the semi-supervised learning setting carry over to the survival analysis context)?, and (2) what is the best approach to integrate the 3 subsets of data in the analysis?
To address this second question, we propose and compare three different approaches.
The first approach is to treat the unlabeled instances as censored with the censoring time equal to zero and apply a machine learning-based survival analysis technique.
For the second approach, we apply a standard semi-supervised learning approach. In particular, we use the widely used self-training wrapper technique [1]}, [2]}. This technique first builds a classifier over the labeled (in our case, observed and censored) data points and iteratively augments the labeled set with highly confident predictions over the unlabeled dataset.
In the third approach, we propose an adaptation of the second one, in which we initially add the censored instances to the unlabeled set, and exploit the censored information in the data augmentation process, to decide how many instances to add to the labeled set in each iteration.
In all three approaches, we use random survival forests as base learner [3]}.
In order to answer the research questions, we apply and compare the approaches using fifteen real-life datasets from the healthcare domain.
